Advances In Numerical Model Data Assimilation And Observations For Hazardous Weather Prediction

Accurate and timely forecasting of hazardous weather events induced by meso-scale convection systems (MCSs) is the key to safeguarding lives and property. Yet the MCS forecasting is challenging due to imperfect initial numerical conditions that lack meso-scale convective information and multi-scale dynamic and thermodynamic consistency. Remote sensing observations are the primary source of estimating weather conditions, such as moisture, wind velocity, and precipitation. It is of fundamental pivotality to develop data assimilation technologies to enhance applications of multi-source observations. Performance assessments of new types of observations facilitate the network designment for regional- and storm-scale numerical models. This Research Topic seeks submissions underscoring the improvement of the accuracy of MCS predictions, warnings, and decision support for high-impact weather events as well as observation network designs.